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      CNN_backbone(1)_AlexNet
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        <!-- toc -->
<ul>
<li><a href="#pass-1">Pass 1</a>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#discussion">Discussion</a></li>
</ul>
</li>
<li><a href="#pass-2">Pass 2</a>
<ul>
<li><a href="#introduction">Introduction</a></li>
<li><a href="#the-dataset">The Dataset</a></li>
<li><a href="#the-architecture">The Architecture</a></li>
<li><a href="#details-in-architecture">Details in Architecture</a></li>
<li><a href="#reducing-overfitting">Reducing Overfitting</a></li>
<li><a href="#details-of-learning">Details of learning</a></li>
<li><a href="#qualitative-evaluations">Qualitative Evaluations</a></li>
</ul>
</li>
<li><a href="#%E8%A1%A5%E5%85%85">补充</a>
<ul>
<li><a href="#%E5%85%B3%E4%BA%8Ealexnet%E5%8D%B7%E7%A7%AF%E5%88%B0%E5%85%A8%E8%BF%9E%E6%8E%A5%E5%A4%84%E7%9A%84%E8%BD%AC%E5%8F%98%E6%96%B9%E5%BC%8F">关于AlexNet卷积到全连接处的转变方式</a></li>
<li><a href="#maxpooling%E7%9A%84%E5%8E%9F%E7%90%86">MaxPooling的原理</a></li>
<li><a href="#dropout%E7%9A%84%E4%B8%A4%E7%A7%8D%E5%AE%9E%E7%8E%B0%E6%96%B9%E5%BC%8F">Dropout的两种实现方式</a>
<ul>
<li><a href="#vanilla-dropout">Vanilla Dropout</a></li>
<li><a href="#inverted-dropout">Inverted Dropout</a></li>
</ul>
</li>
</ul>
</li>
<li><a href="#%E7%9C%8B%E5%AE%8C%E6%89%80%E6%9C%89cnn-backbones%E5%90%8E%E7%9A%84%E4%B8%80%E7%82%B9%E6%84%9F%E6%83%B3">看完所有CNN Backbones后的一点感想</a></li>
</ul>
<!-- tocstop -->
<p>标题：ImageNet Classification with Deep Convolutional Neural Networks</p>
<h2><span id="pass-1">Pass 1</span></h2>
<h3><span id="abstract">Abstract</span></h3>
<ul>
<li>Large, deep convolutional nerual network to classify images</li>
<li>60 million parameters</li>
<li>650,000 neurons</li>
<li>5 convolutional layers</li>
<li>To reduce overfitting, use <strong>dropout</strong> in fc layers</li>
<li>To speed the training, GPU and non-saturating neurons</li>
<li>Dataset: LSVRC-2010 and LSVRC-2012</li>
<li>Result: considerably better than ever</li>
</ul>
<h3><span id="discussion">Discussion</span></h3>
<ul>
<li>Supervised learning, no any supervised pre-training</li>
<li>The depth is important(a loss of 2% if a single conv layer is removed, but it may be influenced by parameters)</li>
<li>Hope: deeper and larger makes better; video sequences</li>
</ul>
<h2><span id="pass-2">Pass 2</span></h2>
<h3><span id="introduction">Introduction</span></h3>
<ul>
<li>
<p>Two chances: large image datasets such as ImageNet, powerful GPUs</p>
</li>
<li>
<p>Compared to standard feedforward neural network with similarly-sized layers, CNNs are <strong>easier to train</strong> because of much fewer connections and parameters, while their theoretically-best <strong>performance</strong> is likely to be <strong>only slightly worse</strong>.</p>
</li>
<li>
<p>Largest conv nn, highly-optimized GPU implementation of 2D convolution</p>
</li>
<li>
<p>Datasets: ILSVRC-2010, ILSVRC-2012</p>
</li>
<li>
<p><strong>Lots of new and unusual features</strong> which improves performance and reduces training time</p>
</li>
<li>
<p>Less training-time</p>
</li>
<li>
<p>Problem: overfitting</p>
</li>
<li>
<p>5 conv-layers(each of which contains no more than 1% of the model’s parameters) + 3 fc-layers</p>
</li>
<li>
<p>Limits: memory of current GPUs and training time</p>
</li>
</ul>
<h3><span id="the-dataset">The Dataset</span></h3>
<ul>
<li>Training set: ILSVRC-2010</li>
<li>Testing set: ILSVRC-2012</li>
<li>Assessment: top-1 and top-5 error rates</li>
<li>Centered(256*256) raw RGB values of the pixels(<strong>end2end</strong>)
<ul>
<li>variable-resolution images, while the system requires a constant input dimensionality——<strong>down-sample</strong> the images to a fixed resolution of 256 x 256</li>
<li>rectangular: <strong>rescale the short side</strong> to length 256</li>
</ul>
</li>
</ul>
<h3><span id="the-architecture">The Architecture</span></h3>
<p>5 conv-layers + 3 fc-layers + 1000-way  softmax</p>
<ol>
<li>ReLU: learn faster, compared to f(x)=|tanh(x)|, prevent overfit<font color="red">(?)</font></li>
<li>Two GPUs parallelization, without going through host machine memory; the GPUs communicate only in certain layers; layers in one model community in the same GPU</li>
<li>Local normalization scheme aids generalization</li>
<li>Summarize the outputs of neighboring groups of neurons in the same kernel map, to reduce overfit slightly</li>
</ol>
<h3><span id="details-in-architecture">Details in Architecture</span></h3>
<ul>
<li>受于GPU内存限制，用了两个GPU</li>
<li>从第1层卷积层到第2层卷积层，都只拿自己所在GPU的结果，两个GPU之间没有任何通讯。</li>
<li>从第2层卷积层到第3层卷积层，两个GPU之间有通讯。在输出通道维度合并。</li>
<li>高宽被压缩，空间信息被压缩；通道数增加，每一个通道可以看成在识别某个特定模式，比如说这个通道在识别猫腿，那个通道在识别颜色。、</li>
<li>整个CNN可看作将raw images提取特征，获得机器能够看懂的长为4096的向量，向量相似，则图片相似。换句话说，CNN通过某种翻译方式将像素翻译成了向量。</li>
<li>（能否创建一个翻译的标准库，固定翻译的方式？）</li>
<li><strong>知识的压缩</strong>，个人认为是<font color="red">翻译</font>。</li>
<li>关于将模型切开，在两个CPU上训练，实际上是一个工程上的细节，但通用性和必要性不强。就算是做多GPU训练，也有其他方法。然而最近发现，在更大的模型出现后，比如说GBDT BERT，大家发现还是得切开模型，目前特别是在NLP中成为一个主流。</li>
</ul>
<h3><span id="reducing-overfitting">Reducing Overfitting</span></h3>
<ul>
<li><strong>Data Augmentation</strong>
<ul>
<li><strong>extract random</strong> 224 x 224 patches, otherwise it suffers from substantial overfitting</li>
<li><strong>alter the intensities of the RGB using PCA</strong>, 使得每次选取的图片在颜色上存在差异。</li>
</ul>
</li>
<li><strong>Dropout</strong>: Every time an input is presented, the neural network samples a different architecture, but all these architectures share weights.  reduces overfit, doubles the iterations, reduces complex co-adaptations of neurons.(dropout实际上等价于L2正则项)</li>
</ul>
<h3><span id="details-of-learning">Details of learning</span></h3>
<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><span class="line">SGD <span class="comment"># 其噪音有利于模型泛化</span></span><br><span class="line"></span><br><span class="line">batch_size = <span class="number">128</span></span><br><span class="line">momentum = <span class="number">0.9</span> <span class="comment"># 当优化表明不那么平滑时，保持一种惯性，使其梯度下降方向变化不至于太大。</span></span><br><span class="line">weight_decay = <span class="number">0.0005</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># init</span></span><br><span class="line">weights: a zero-mean Gaussian Distribution <span class="keyword">with</span> standard deviation <span class="number">0.01</span>(方差)</span><br><span class="line"></span><br><span class="line">biases: constant <span class="number">1</span> <span class="keyword">in</span> 2nd, 4th, 5th conv-layers <span class="keyword">and</span> <span class="keyword">in</span> the fc-hidden-layers</span><br><span class="line">biases: constant <span class="number">0</span> <span class="keyword">in</span> the remaining layers</span><br><span class="line"></span><br><span class="line">learning rate: when the validation error rate stopped improving <span class="keyword">with</span> the current learning rate, divide it by <span class="number">10</span></span><br></pre></td></tr></table></figure>
<h3><span id="qualitative-evaluations">Qualitative Evaluations</span></h3>
<p>the conv kernels are learned, or more accurately, <strong>specialized during every run</strong> and is <strong>independent</strong> of any particular random weight initialization.</p>
<p>it looks like decision tree, but CNNs not only do classification but also do feature extraction itself.</p>
<p>(现在的研究工作认为神经元学习到的东西是有对应性的，底层的神经元学习到的一般是比较局部的东西，比如说纹理；偏上的神经元一般学到的是更全局的，比如说这是一个头、这是一种动物)</p>
<h2><span id="补充">补充</span></h2>
<p>（注：补充信息涉及到内容包括但不限于AlexNet中的内容）</p>
<h3><span id="关于alexnet卷积到全连接处的转变方式">关于AlexNet卷积到全连接处的转变方式</span></h3>
<p>这里并不是使用的展开再concatenate，而是继续使用的卷积。具体情况如下图所示：</p>
<p><img src="https://gitee.com/tina-yao/bigbig-shark/raw/master/imgs/AlexNet/1" alt="image-20220801190338513"></p>
<h3><span id="maxpooling的原理">MaxPooling的原理</span></h3>
<p>已知MaxPooling是怎么做的，那么其原理究竟是什么？</p>
<ul>
<li>直接实际意义：只取得其中最大的那个Pooling层作为保留值，其他特征值全部抛弃，表明只保留这些特征中最强的，抛弃其他弱的此类特征。</li>
<li>原理：
<ul>
<li>平移不变性、旋转不变性、保持了特征的相对位置关系。</li>
</ul>
</li>
<li>作用：
<ul>
<li>减少了模型参数</li>
<li>减少了过拟合问题</li>
</ul>
</li>
<li>缺点：
<ul>
<li>特征信息丢失</li>
<li>有些强特征会出现多次</li>
</ul>
</li>
<li>改进：
<ul>
<li>是否可以让神经网络自己选择Pooling的方式，模仿Inception，选出最合适的关键特征信息：这样依旧能够发挥Pooling的作用，且对特征的选取更为合适。</li>
</ul>
</li>
</ul>
<h3><span id="dropout的两种实现方式">Dropout的两种实现方式</span></h3>
<p><a target="_blank" rel="noopener" href="https://cs231n.github.io/neural-networks-2/#reg">cs231n的详细讲解笔记</a></p>
<h4><span id="vanilla-dropout">Vanilla Dropout</span></h4>
<h4><span id="inverted-dropout">Inverted Dropout</span></h4>
<h2><span id="看完所有cnn-backbones后的一点感想">看完所有CNN Backbones后的一点感想</span></h2>
<p>不管是AlexNet，还是ResNet，都是在前人的基础上完成的；关键在于站在巨人的肩膀上后，如何去突破。</p>
<p>我们后人看前辈的杰出工作，因为早已浸淫在前辈创造出的环境中，所以当我会过头去看他们当年的工作时，觉得理所当然、自然而然。当然，这就是象棋中所谓“马后炮”。</p>

      
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